The term computed tomography (CT) usually refers to processes whereby one or more images representing essentially any desired view of the internal structures of a physical object of interest are computed from a corresponding set of images representing respective geometric projections of the object.
To acquire the projection images of an object, a tomographic imaging apparatus requires (i) a source of particles or electromagnetic radiation to probe the object, (ii) a detector to measure the resultant probe-object interactions, and (iii) a means for changing the relative orientation between the source/detector components and the object. The projection images constituting the image set thus represent measurements of the probe-object interactions acquired at respective relative orientations between the source/detector components and the object. These directions are typically chosen such that the source and detector follow a particular trajectory relative to the object, the trajectory depending on the geometry between the source and the detector. Examples of such trajectories include circular, helical and saddle trajectories.
Once the set of two-dimensional projection images at respective different relative orientations has been generated, reconstruction algorithms are applied to these images to generate a corresponding data set referred to herein as a tomogram, representing the external and internal structural features of the object in three dimensions. Using the tomogram as input, display software can then be used to visualise the object in essentially any way desired by a user, including as a rotating semi-transparent object, static and dynamic slices through the object along arbitrary directions, and the like. Such ‘reconstructed’ images are referred to herein as tomographic images.
A particular difficulty with computed tomography is that the reconstruction algorithms assume that the three components of the tomographic imaging apparatus or system described above are in perfect mutual alignment. In practice this is rarely, if ever, the case, in particular, for imaging features with micrometer or nanometer dimensions. In such cases, the experiment is said to be ‘misaligned’, causing the reconstructed three-dimensional tomographic images to appear globally or locally “blurry” or “out-of-focus”.
Various attempts have been made to overcome these difficulties. In A. V. Bronnikov, Virtual Alignment of x-ray cone-beam tomography system using two calibration aperture measurements, Opt. Eng. 38(2), 381-386 (1999), a specially manufactured calibration aperture is used in place of an actual object or sample of interest, and a cone-shaped x-ray beam is used to generate projection images of the aperture for opposite alignments of the aperture. These images are then processed to determine lateral and rotational misalignments of the rotational axis. Once measured in this manner, these misalignments can be used to modify projection images of actual samples of interest to compensate for the misalignments before applying standard reconstruction algorithms to the modified images.
Alternatively, the measured misalignments can be used as input to a modified reconstruction algorithm that corrects for some forms of misalignment, as described in M. Karolczak et. al., Implementation of a cone-beam reconstruction algorithm for the single-circle source orbit with embedded misalignment correction using homogeneous coordinates, Med. Phys. 28 (10), 2050-2069, 2001.
However, existing methods for correcting or compensating for misalignments of a tomographic apparatus are limited in their accuracy and applicability. It is desired, therefore, to provide a computed tomography imaging process and system that alleviate one or more difficulties of the prior art, or that at least provide a useful alternative.